'''simple docstring'''




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        a__ (  unittest.TestCase					):






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         torch.manual_seed(0       )
         __lowerCamelCase            =    UNetaDModel(
             block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
         return model






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    self.dummy_uncond_unet
         __lowerCamelCase            =    ScoreSdeVeScheduler()

         __lowerCamelCase            =    ScoreSdeVePipeline(unet=a    ,							scheduler=a       )
         sde_ve.to(a       )
         sde_ve.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    sde_ve(num_inference_steps=2    ,							output_type='''numpy'''    ,							generator=a       ).images

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    sde_ve(num_inference_steps=2    ,							output_type='''numpy'''    ,							generator=a    ,							return_dict=a       )[
             0
         ]

         __lowerCamelCase            =    image[0, -3:, -3:, -1]
         __lowerCamelCase            =    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         __lowerCamelCase            =    np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2




@slow
@require_torch
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    '''google/ncsnpp-church-256'''
         __lowerCamelCase            =    UNetaDModel.from_pretrained(a       )

         __lowerCamelCase            =    ScoreSdeVeScheduler.from_pretrained(a       )

         __lowerCamelCase            =    ScoreSdeVePipeline(unet=a    ,							scheduler=a       )
         sde_ve.to(a       )
         sde_ve.set_progress_bar_config(disable=a       )

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    sde_ve(num_inference_steps=10    ,							output_type='''numpy'''    ,							generator=a       ).images

         __lowerCamelCase            =    image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)

         __lowerCamelCase            =    np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2

'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
